(1) Number of species in both datasets split up by group

(2) Looking at the total difference in area (km^2) for each species range map in both datasets

There are large differences so to get a better sense, here is the same comparison but with area log transformed.

Data split into species groups and by IUCN category ***

(3) Looking at the overlap between species range maps from IUCN and AquaMaps

This plot shows the total percent of a species range map that is (1) only covered by IUCN cells, (2) only covered by AquaMaps cells and (3) covered by both IUCN and AquaMaps cells.

How this breaks down between AquaMaps and IUCN

Here is an example of a single species, the yellow speckled chromis (Chromis alpha)

For Chromis alpha, 3.14% of the total area is covered by both IUCN and AquaMaps, 3.18% is covered just by AquaMaps and 77.3% is covered just by IUCN.

Now looking across all species, here is the breakdown of how much overlap there is between the two datasets

This plot shows that the majority of species range maps have only ~30% of their full map covered by both maps, and very few species have more than 50% of their two ranges (IUCN and AquaMaps) overlapping.

Split up by species - not sure if this is useful or not

(4) Looking at the percent of smaller species range that is within the larger range.

For each range

Exploring possible correlation between total species range area and percent overlap of smaller range within the larger: 

spp_list1 <- spp_list %>%
  group_by(spp_group) %>%
  mutate(n_spp = n()) %>%
  ungroup()

spp_gp_q1 <- spp_list1 %>%
  filter(area_ratio >= median(area_ratio, na.rm = TRUE) & sm_perc >= median(sm_perc, na.rm = TRUE)) %>%
  group_by(spp_group) %>%
  summarize(n_spp = first(n_spp),
            n_spp_q1 = n())
spp_gp_q2 <- spp_list1 %>%
  filter(area_ratio < median(area_ratio, na.rm = TRUE) & sm_perc >= median(sm_perc, na.rm = TRUE)) %>%
  group_by(spp_group) %>%
  summarize(n_spp = first(n_spp),
            n_spp_q2 = n())
spp_gp_q4 <- spp_list1 %>%
  filter(area_ratio < median(area_ratio, na.rm = TRUE) & sm_perc < median(sm_perc, na.rm = TRUE)) %>%
  group_by(spp_group) %>%
  summarize(n_spp = first(n_spp),
            n_spp_q4 = n())
spp_gp_q3 <- spp_list1 %>%
  filter(area_ratio >= median(area_ratio, na.rm = TRUE) & sm_perc < median(sm_perc, na.rm = TRUE)) %>%
  group_by(spp_group) %>%
  summarize(n_spp = first(n_spp),
            n_spp_q3 = n())

spp_gp_quadrants <- spp_gp_q1 %>%
  left_join(spp_gp_q2, by = c('spp_group', 'n_spp')) %>%
  left_join(spp_gp_q3, by = c('spp_group', 'n_spp')) %>%
  left_join(spp_gp_q4, by = c('spp_group', 'n_spp')) %>%
  gather(quad, n_quad, n_spp_q1, n_spp_q2, n_spp_q3, n_spp_q4) %>% 
  mutate(quad = str_replace(quad, 'n_spp_', ''),
         pct_quad = n_quad/n_spp)

spp_gp_quadrants <- spp_gp_quadrants %>%
  left_join(spp_gp_quadrants %>% 
              filter(quad == 'q1') %>%
              select(spp_group, pct_q1 = pct_quad),
            by = 'spp_group')

barchart_spp_gp_quads <- ggplot(spp_gp_quadrants %>% 
                                  transform(spp_group = reorder(spp_group, pct_q1)), 
                                aes(x = spp_group, fill = quad, weight = pct_quad)) +
  ggtheme_plot + 
  geom_bar(stat = 'bin') +
  geom_text(aes(label = sprintf('n = %s', n_spp), y = .05), hjust = 0) +
  coord_flip() +
  labs(x = 'Species Group', 
       y = 'Relative number of species per quadrant', 
       title = 'Species group breakdown by quadrant')

## 
## Call:
## lm(formula = total_area ~ sm_perc, data = spp_list)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -31528842 -12533425  -1793686   5117681 341380989 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -10860595    2496562   -4.35 1.43e-05 ***
## sm_perc        425500      36394   11.69  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33120000 on 1885 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.06761,    Adjusted R-squared:  0.06712 
## F-statistic: 136.7 on 1 and 1885 DF,  p-value: < 2.2e-16

Percent coverage as function of area, clipping to area below 100,000,000 km^2:

Percent coverage as a function of log(area)

(5) Examining data broken into quartiles to see if smaller patterns emerge

(6) Looking at how many and what type (threat category) of species we are losing by setting a threshold of 0.4 on the aquamaps species

This looks at the full Aquamaps dataset… should it be limited to species with valid categories?

Changes in SPP for scenarios

Comparing three scenarios to the current (v2015) global model for SPP:

Boxplots:

Absolute change in SPP score

Relative change in SPP score